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In our research, we aimed to address the shortcomings of traditional fruit image classification models, which struggle with inconsistent lighting, complex backgrounds, and high computational demands. To overcome these challenges, we developed a novel multi-label classification method incorporating advanced image preprocessing techniques, such as Contrast Limited Adaptive Histogram Equalization and the Gray World algorithm, which enhance image quality and color balance. Utilizing lightweight encoder–decoder architectures, specifically MobileNet, DenseNet, and EfficientNet, optimized with an Asymmetric Binary Cross-Entropy Loss function, we improved model performance in handling diverse sample difficulties. Furthermore, Multi-Label Knowledge Distillation (MLKD) was implemented to transfer knowledge from large, complex teacher models to smaller, efficient student models, thereby reducing computational complexity without compromising accuracy. Experimental results on the DeepFruit dataset, which includes 21,122 images of 20 fruit categories, demonstrated that our method achieved a peak mean Average Precision (mAP) of 90.2% using EfficientNet-B3, with a computational cost of 7.9 GFLOPs. Ablation studies confirmed that the integration of image preprocessing, optimized loss functions, and knowledge distillation significantly enhances performance compared to the baseline models. This innovative method offers a practical solution for real-time fruit classification on resource-constrained devices, thereby supporting advancements in smart agriculture and the food industry. [ABSTRACT FROM AUTHOR]

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial for pre-emptive interventions. Integrating genetic and clinical data from 421287 UK Biobank and 8900 Nanfang Hospital participants, a stacked multimodal machine learning model is developed and validated to synthesize its probabilities as an in-silico quantitative marker for hyperuricemia (ISHUA). The model demonstrates satisfactory performance in detecting HUA, exhibiting area under the curves (AUCs) of 0.859, 0.836, and 0.779 within the train, internal, and external test sets, respectively. ISHUA is significantly associated with gout and metabolism-related outcomes, effectively classifying individuals into low- and high-risk groups for gout in the train (AUC, 0.815) and internal test (AUC, 0.814) sets. The high-risk group shows increased susceptibility to metabolism-related outcomes, and participants with intermediate or favorable lifestyle profiles have hazard ratios of 0.75 and 0.53 for gout compared with those with unfavorable lifestyles. Similar trends are observed for other metabolism-related outcomes. The multimodal machine learning-based ISHUA marker enables personalized risk stratification for gout and metabolism-related outcomes, and it is unveiled that lifestyle changes can ameliorate these outcomes within high-risk group, providing guidance for preventive interventions., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

The utilization of vast amounts of EHR data is crucial to the studies in medical informatics. Physicians are medical participants who directly record clinical data into EHR with their personal expertise, making their roles essential in follow-up data utilization, which current studies have yet to recognize. This paper proposes a physician-centered perspective for EHR data utilization and emphasizes the feasibility and potentiality of digging into physicians' latent decision patterns in EHR. To support our proposal, we design a physician-centered CDS approach named PhyC and test it on a real-world EHR dataset. Experiments show that PhyC performs significantly better in the auxiliary diagnosis of multiple diseases than globally learned models. Discussions on experimental results suggest that physician-centered data utilization can help to derive more objective CDS models, while more means for utilization need further exploration.